Intro to ggplot2 - Session 1
Katie Sasso
June 07, 2018
Visualization with R
- R has a variety of visualization tools.
- These tools can be general and applied to any field or data.
- Some packages can also be very field specific.
There are 3 main families of visualization functions:
- Base R
plot()– See?plot
- Lattice – See
?lattice::Lattice
- ggplot2 – See
?ggplot2::ggplot2
- Let’s check out The R Graph Gallery
Base R visualization
- Simplest plotting in R
- Not very pretty
- Difficult to customize
- Not so bad if you just want to use it for quick data exploration
Basic plot syntax:
plot(x , y) x: vector for x axis, y: vector for y axis
See ?plot
x <- 1:10
y <- 1:10
plot(x, y)
Base R visualization: Scatterplot with iris
plot(iris$Sepal.Width, iris$Sepal.Length)
Base R visualization: Histogram with iris
hist(iris$Sepal.Width)
Base R visualization: Using par() to plot multiple plots
par(mfrow=c(1,2))
plot(iris$Sepal.Width, iris$Sepal.Length)
hist(iris$Sepal.Width)
plot() vs ggplot()
- A picture is worth a thousand words – when the picture is good
Add layers to ggplot()
And make it interactive with ggplotly()
ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics
- A system for ‘declaratively’ creating graphics, based on “The Grammar of Graphics”.
- You provide the data, tell
ggplot2how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.- Infinite options for the ultimate customization
- It is part of the tidyverse, a collection of R packages that share common philosophies and are designed to work together.
Installation
# The easiest way to get ggplot2 is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just ggplot2:
install.packages("ggplot2")
# Don't forget to load tidyverse to your environment
library(tidyverse)
# Or just ggplot2
library(ggplot2)Usage
- Start with
ggplot(),- supply a dataset
- and aesthetic mapping using
aes().
- supply a dataset
- You can then add on layers such as:
- Geom (geometric object) with various
geom_functions. - Scales with various
scale_orlabs()andlims()functions. - Faceting specifications with
facet_functions
- Coordinate systems with
coord_functions
- Geom (geometric object) with various
Building a ggplot from scratch with iris
Step 0: Let’s remember the iris data
head(iris, 3)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosasummary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## Step 1. Define data and aesthetics woth aes()
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p
Step 2. Define plot type with geom_
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point()
Step 3. Assign more aesthetics
Step 3.1 Add color
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species))
Step 3.2 Add color + size
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species, size=Petal.Length))
Step 3.3 Add color + size + alpha (transparency)
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species, size=Petal.Length, alpha=Petal.Width))
Step 3.4 Add color + size + alpha + shape
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species, size=Petal.Length, alpha=Petal.Width, shape=Species))
Step 4. Customize legend
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species, size=Petal.Length, alpha=Petal.Width, shape=Species)) +
guides( color=guide_legend(ncol = 3, byrow = TRUE),
size=guide_legend(ncol = 3, byrow = TRUE),
alpha=guide_legend(ncol = 3, byrow = TRUE))
Step 5: Assign more geom: point + smooth
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(color=Species, size=Petal.Length, alpha=Petal.Width)) +
geom_smooth()What will this give me?
Ooops! What happened??
- Smooth line was for the entire data and not for each of the Iris species.
- Why?
Step 5.1: Assign more geom: point + smooth
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length, color=Species))
p + geom_point(aes(size=Petal.Length, alpha=Petal.Width)) +
geom_smooth()
Why did this work now?
Can you see the difference?
Step 5.2: Assign more geom: point + smooth
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length))
p + geom_point(aes(size=Petal.Length, alpha=Petal.Width)) + geom_smooth(aes(color=Species))
What about this? What’s happening here?
Now the color is only defined in the geom_smooth and not for geom_point
Step 6: Facetting
Step 6.0: Create a toy dataset
Let’s generate a hypothetical iris with some added ecosystem type and precipitation data.
- 3 types of ecosystem: Forest, Riparian, Urban
- 2 types of precipitation: Heavy, Mild
Here I am using the sample() function which allows me to randomly pick variables from a vector.
I assign the 150 variables I want to pick with size = 150 and replace=TRUE allows me to pich these variables multiple times.
Then I assign these to my new dataset iris2 with column names Ecosystem and Precipitation
ecosys <- sample(c("Forest", "Riparian", "Urban"), size = 150, replace = T)
precp <- sample(c("Heavy", "Mild"), size = 150, replace = T)
iris2 <- cbind(iris, Ecosystem=ecosys, Precipitation=precp)
head(iris2)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Ecosystem
## 1 5.1 3.5 1.4 0.2 setosa Riparian
## 2 4.9 3.0 1.4 0.2 setosa Urban
## 3 4.7 3.2 1.3 0.2 setosa Riparian
## 4 4.6 3.1 1.5 0.2 setosa Urban
## 5 5.0 3.6 1.4 0.2 setosa Riparian
## 6 5.4 3.9 1.7 0.4 setosa Urban
## Precipitation
## 1 Heavy
## 2 Heavy
## 3 Heavy
## 4 Mild
## 5 Heavy
## 6 MildStep 6.1: Facet iris2
Now, I would like to see how my previous graph changes for the different types of ecosystem and precipitation.
This was the graph :
- I am not using
geom_smoothfor now because I do not have enough data points for model prediction.
- Also I will remove the
alphaaesthetic to make it easier for us to see.
p2 <- ggplot(data=iris2, aes(x=Sepal.Width, y=Sepal.Length, color=Species))
p2 <- p2 + geom_point(aes(size=Petal.Length)) # + geom_smooth()
p2
Now I add facets!
p2 + facet_grid(Ecosystem ~ Precipitation) 
I can customize the facets very easily!
p2 + facet_grid( . ~ Precipitation) 
p2 + facet_grid(Ecosystem ~ .) 
p2 + facet_grid(Precipitation ~ .) 
You get the idea here right?
Step 6.2: Facet wages
You can use facet_wrap if you want to facet by just 1 variable but you want to organize them nicely.
First, let’s read the wages data in R.
wages <- read.csv("./wages.csv",
header = T,
stringsAsFactors = T)
head(wages, 3)## earn height sex race ed age
## 1 79571.30 73.89 male white 16 49
## 2 96396.99 66.23 female white 16 62
## 3 48710.67 63.77 female white 16 33Let’s create age categories with cut() function. I am turning the continous age varialbe into a categorical variable with cut() function. I am also setting categorical intervals increasing by 10 years with the breaks = seq(20, 100, by=10) argument. Then I assign this new variable to a new column called age_cat.
wages <- wages %>% mutate(age_cat = cut(age, breaks = seq(20, 100, by=10)) )
head(wages, 4)## earn height sex race ed age age_cat
## 1 79571.30 73.89 male white 16 49 (40,50]
## 2 96396.99 66.23 female white 16 62 (60,70]
## 3 48710.67 63.77 female white 16 33 (30,40]
## 4 80478.10 63.22 female other 16 95 (90,100]Let’s plot it
pw <- ggplot(wages, aes(x=height, y=earn)) +
geom_point(aes(size=ed), alpha=0.5)
pw
pw + facet_wrap(~age_cat)
Or you can specify the rows and columns for the faceting
pw + facet_wrap(~age_cat, ncol=5)
Your turn (5 mins)
Plot the wages.csv data like the following
